Many technological fields deal with visualization of uncertain data on various objects. One, though a non-limiting example of such uncertain data is an image of a complex, multilayer physical object; indeed, a single image physically cannot comprise accurate information on all layers of the object. Structure of such objects may be studied by deeply analyzing available images thereof. Examples of the imaging and further analysis may be found in meteorology, in geophysics, biology, medicine, in medical equipment technologies such as Computer Tomography, in inspection of modern 3D semiconductor structures, etc. The images to be analyzed may be obtained by various technologies utilized in the corresponding fields.
In the present patent application, the technique of interest will be described, inter alia, by using a non-limiting example from the field of inspection of modern multilayer semiconductor structures.
The modern multilayer semiconductor structures of interest have arrived to such a scale of miniaturization (presently, up to nodes scale of about 7-10 nm) that they can hardly be inspected with required accuracy by optical microscopes which provide visual images.
An alternative, more practical option is to utilize technologies involving more advanced tools than optical microscopes. Scanning electron microscopes (SEM) are often used in inspection of semiconductor wafers in order to detect and classify defects in production of microelectronic devices. SEM images, however, contain a wealth of detail, which must be properly interpreted in order to identify the structures appearing in each image, to distinguish the structures from other features and to estimate their relative coordinates.
In order to proceed with the description, some comprehensive definitions have been introduced below, which are important for understanding the problem and the exemplary solutions which will be described below.
Three-dimensional integrated circuit (3D IC)—an integrated circuit manufactured by stacking silicon wafers and/or dies and interconnecting them vertically using through-silicon vias (TSVs) so that they behave as a single device. 3D IC is an example of a Multilayer structure.
SEM—Scanning Electron Microscope used for exposing a 3D IC to a primary electron beam, collecting data on responsive electron beams or scattering electrons from multiple layers of the 3D IC and further reconstructing a combined SEM image of the multiple layers by applying computer processing to the collected data.
Available (Initial) image—a digital image, or a set of digital images of an object to be analyzed, for example in the form of a grey scale image. SEM-image—one example of Available (Initial) image. Available image may be planar or three-dimensional.
Segmentation of available image—labeling pixels of the available image to associate them with two or more different classes of features (objects, elements). The features may be located at different layers of the object(s) to be analyzed. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in the images.
More precisely, image segmentation is a process of assigning a label to every pixel in an image, such that pixels with the same label share certain characteristics.
Segmentation of SEM-image—labeling pixels of a SEM-image of a group or system of objects by classes of features, wherein the features may be located at different objects/areas/portions/of the group, for example at different layers of that group of objects.
Various techniques are known in the art for automated segmentation of SEM images to assist in proper image interpretation thereof.
Segmentation results—comprise information 1 about labeling of each specific pixel of an Initial image, and information 2 about uncertainty of the labeling of each specific pixel, the information 2 being also called segmentation uncertainty or segmentation uncertainty measure.
Pixel—will be used to indicate a minimal portion/cell/unit of an image.
Methods of image segmentation have a wide range of applications, such as analysis of medical data, visualization of bio-informatics data, remote sensing studies, etc. Uncertainty arises from measurement inaccuracies and from predication errors, as in the case of image segmentation problems. Probabilistic approaches to image segmentation associate a vector of uncertainty values to each pixel. Each value in this vector describes the uncertainty of determining whether the pixel is a member of a specific segment or not. The visualization task is then carried out by assigning a unique color for each segment and by defining a mapping from the uncertainty vector values to specific color coordinates. In the volumetric case, opacity may be determined as well. Currently available color mappings are based on maximum probability (also known as tagged data), on probability thresholding; on probability ratios (risk values); and on entropy values. The uncertainty measure in these cases is used for choosing one of the colors for a segment and for setting either its intensity or saturation value. In such a case, the obtained/displayed image does not reflect pixel values of the initial image any more. Rather, it reflects the segmentation uncertainty only.
To the best of the Applicant's knowledge, there is still a long felt need for a simple and effective technique for visualization of elements in an available image.